{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial Attacks Example in PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "17loqDVddeFB"
   },
   "source": [
    "## Import Dependencies\n",
    "\n",
    "This section imports all necessary libraries, such as PyTorch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eDXHEl0AdU3q"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import math\n",
    "\n",
    "import torch.backends.cudnn as cudnn\n",
    "import os\n",
    "import argparse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nlTHx8sOdg27"
   },
   "source": [
    "### GPU Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 900,
     "status": "ok",
     "timestamp": 1560944452374,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "3w_z5lpRds2T",
    "outputId": "c37de9a4-2bc0-488b-8f71-dd75b16d15f5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using CPU.\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "if torch.cuda.is_available():\n",
    "  print(\"Using GPU.\")\n",
    "else: \n",
    "  print(\"Using CPU.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2fjrmAzkdtqH"
   },
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1347,
     "status": "ok",
     "timestamp": 1560944456251,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "2DweIjUMdxFm",
    "outputId": "3b1ef0e2-219b-4b80-bd00-95351cb70e60"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Preparing data..\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 99%|█████████▉| 9846784/9912422 [00:12<00:00, 991239.70it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "32768it [00:00, 49214.56it/s]                           \u001b[A\n",
      "\n",
      "0it [00:00, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
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      "1654784it [00:04, 331842.22it/s]                             \u001b[A\n",
      "\n",
      "0it [00:00, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "8192it [00:00, 17854.49it/s]            \u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# MNIST dataloader declaration\n",
    "\n",
    "print('==> Preparing data..')\n",
    "\n",
    "# The standard output of the torchvision MNIST data set is [0,1] range, which\n",
    "# is what we want for later processing. All we need for a transform, is to \n",
    "# translate it to tensors.\n",
    "\n",
    "# We first download the train and test datasets if necessary and then load them into pytorch dataloaders.\n",
    "mnist_train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)\n",
    "mnist_test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)\n",
    "\n",
    "\n",
    "mnist_dataset_sizes = {'train' : mnist_train_dataset.__len__(), 'test' : mnist_test_dataset.__len__()} # a dictionary to keep both train and test datasets\n",
    "\n",
    "mnist_train_loader = torch.utils.data.DataLoader(\n",
    "                 dataset=mnist_train_dataset,\n",
    "                 batch_size=256,\n",
    "                 shuffle=True)\n",
    "mnist_test_loader = torch.utils.data.DataLoader(\n",
    "                dataset=mnist_test_dataset,\n",
    "                batch_size=1,\n",
    "                shuffle=True)\n",
    "\n",
    "mnist_dataloaders = {'train' : mnist_train_loader ,'test' : mnist_test_loader}  # a dictionary to keep both train and test loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2702,
     "status": "ok",
     "timestamp": 1560944463208,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "6asXL_9mAnvY",
    "outputId": "9494f003-4238-4daa-af0d-6f518e64ca35"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "0it [00:00, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Preparing data..\n",
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "name": "stderr",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# CIFAR10 dataloader declaration\n",
    "\n",
    "print('==> Preparing data..')\n",
    "\n",
    "# The standard output of the torchvision CIFAR data set is [0,1] range, which\n",
    "# is what we want for later processing. All we need for a transform, is to \n",
    "# translate it to tensors.\n",
    "\n",
    "# we first download the train and test datasets if necessary and then load them into pytorch dataloaders\n",
    "cifar_train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
    "cifar_train_loader = torch.utils.data.DataLoader(cifar_train_dataset, batch_size=128, shuffle=True, num_workers=2)\n",
    "\n",
    "cifar_test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.ToTensor())\n",
    "cifar_test_loader = torch.utils.data.DataLoader(cifar_test_dataset, batch_size=100, shuffle=False, num_workers=2)\n",
    "\n",
    "# these are the output categories from the CIFAR dataset\n",
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NZx4ThVgd7Ad"
   },
   "source": [
    "## Model Definition\n",
    "\n",
    "We used LeNet model to train against MNIST dataset because the dataset is not very complex and LeNet can easily reach a high accuracy to then demonstrate an ttack. For CIFAR10 dataset, however, we used the more complex DenseNet model to reach an accuracy of 90% to then attack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LeNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yee8Mby2d6Hl"
   },
   "outputs": [],
   "source": [
    "# LeNet Model definition\n",
    "class LeNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LeNet, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n",
    "        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n",
    "        self.conv2_drop = nn.Dropout2d()\n",
    "        self.fc1 = nn.Linear(320, 50)\n",
    "        self.fc2 = nn.Linear(50, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = F.relu(F.max_pool2d(self.conv1(x), 2))  #first convolutional layer\n",
    "        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2)) #secon convolutional layer with dropout\n",
    "        x = x.view(-1, 320)   #making the data flat\n",
    "        x = F.relu(self.fc1(x)) #fully connected layer\n",
    "        x = F.dropout(x, training=self.training) #final dropout\n",
    "        x = self.fc2(x) # last fully connected layer\n",
    "        return F.log_softmax(x, dim=1) #output layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "G9MAzraE7jgn"
   },
   "source": [
    "This is the standard implementation of the DenseNet proposed in the following paper.\n",
    "[DenseNet paper](https://arxiv.org/abs/1608.06993)\n",
    "\n",
    "The idea of Densely Connected Networks is that every layer is connected to all its previous layers and its succeeding ones, thus forming a Dense Block.\n",
    "\n",
    "![alt text](https://cdn-images-1.medium.com/freeze/max/1000/1*04TJTANujOsauo3foe0zbw.jpeg?q=20)\n",
    "\n",
    "The implementation is broken to smaller parts, called a Dense Block with 5 layers. Each time there is a convolution operation of the previous layer, it is followed by concatenation of the tensors. This is allowed as the channel dimensions, height and width of the input stay the same after convolution with a kernel size 3×3 and padding 1.\n",
    "In this way the feature maps produced are more diversified and tend to have richer patterns. Also, another advantage is better information flow during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DenseNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6-OGOBcYEJXn"
   },
   "outputs": [],
   "source": [
    "# This is a basic densenet model definition.\n",
    "\n",
    "class Bottleneck(nn.Module):\n",
    "    def __init__(self, in_planes, growth_rate):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.bn1 = nn.BatchNorm2d(in_planes)\n",
    "        self.conv1 = nn.Conv2d(in_planes, 4*growth_rate, kernel_size=1, bias=False)\n",
    "        self.bn2 = nn.BatchNorm2d(4*growth_rate)\n",
    "        self.conv2 = nn.Conv2d(4*growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.conv1(F.relu(self.bn1(x)))\n",
    "        out = self.conv2(F.relu(self.bn2(out)))\n",
    "        out = torch.cat([out,x], 1)\n",
    "        return out\n",
    "\n",
    "\n",
    "class Transition(nn.Module):\n",
    "    def __init__(self, in_planes, out_planes):\n",
    "        super(Transition, self).__init__()\n",
    "        self.bn = nn.BatchNorm2d(in_planes)\n",
    "        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.conv(F.relu(self.bn(x)))\n",
    "        out = F.avg_pool2d(out, 2)\n",
    "        return out\n",
    "\n",
    "\n",
    "class DenseNet(nn.Module):\n",
    "    def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10):\n",
    "        super(DenseNet, self).__init__()\n",
    "        self.growth_rate = growth_rate\n",
    "\n",
    "        num_planes = 2*growth_rate\n",
    "        self.conv1 = nn.Conv2d(3, num_planes, kernel_size=3, padding=1, bias=False)\n",
    "\n",
    "        self.dense1 = self._make_dense_layers(block, num_planes, nblocks[0])\n",
    "        num_planes += nblocks[0]*growth_rate\n",
    "        out_planes = int(math.floor(num_planes*reduction))\n",
    "        self.trans1 = Transition(num_planes, out_planes)\n",
    "        num_planes = out_planes\n",
    "\n",
    "        self.dense2 = self._make_dense_layers(block, num_planes, nblocks[1])\n",
    "        num_planes += nblocks[1]*growth_rate\n",
    "        out_planes = int(math.floor(num_planes*reduction))\n",
    "        self.trans2 = Transition(num_planes, out_planes)\n",
    "        num_planes = out_planes\n",
    "\n",
    "        self.dense3 = self._make_dense_layers(block, num_planes, nblocks[2])\n",
    "        num_planes += nblocks[2]*growth_rate\n",
    "        out_planes = int(math.floor(num_planes*reduction))\n",
    "        self.trans3 = Transition(num_planes, out_planes)\n",
    "        num_planes = out_planes\n",
    "\n",
    "        self.dense4 = self._make_dense_layers(block, num_planes, nblocks[3])\n",
    "        num_planes += nblocks[3]*growth_rate\n",
    "\n",
    "        self.bn = nn.BatchNorm2d(num_planes)\n",
    "        self.linear = nn.Linear(num_planes, num_classes)\n",
    "\n",
    "    def _make_dense_layers(self, block, in_planes, nblock):\n",
    "        layers = []\n",
    "        for i in range(nblock):\n",
    "            layers.append(block(in_planes, self.growth_rate))\n",
    "            in_planes += self.growth_rate\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = self.conv1(x)\n",
    "        out = self.trans1(self.dense1(out))\n",
    "        out = self.trans2(self.dense2(out))\n",
    "        out = self.trans3(self.dense3(out))\n",
    "        out = self.dense4(out)\n",
    "        out = F.avg_pool2d(F.relu(self.bn(out)), 4)\n",
    "        out = out.view(out.size(0), -1)\n",
    "        out = self.linear(out)\n",
    "        return out\n",
    "\n",
    "# This creates a densenet model with basic settings for cifar.\n",
    "def densenet_cifar():\n",
    "    return DenseNet(Bottleneck, [6,12,24,16], growth_rate=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4759,
     "status": "ok",
     "timestamp": 1560944481762,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "NFABsDtufRik",
    "outputId": "281f0f13-b738-45fb-9dcf-f2032ade665a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Building the model for MNIST dataset..\n"
     ]
    }
   ],
   "source": [
    "#building model for MNIST data\n",
    "print('==> Building the model for MNIST dataset..')\n",
    "mnist_model = LeNet().to(device)\n",
    "mnist_criterion = nn.CrossEntropyLoss()\n",
    "mnist_optimizer = optim.Adam(mnist_model.parameters(), lr=0.001)\n",
    "mnist_num_epochs= 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 951,
     "status": "ok",
     "timestamp": 1560944484643,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "kq_fHH4nFkd8",
    "outputId": "6c4bc51f-b516-4c36-99d3-c4051f4b1fb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==> Building the model for CIFAR10 dataset..\n"
     ]
    }
   ],
   "source": [
    "#building model for CIFAR10\n",
    "# Model\n",
    "print('==> Building the model for CIFAR10 dataset..')\n",
    "\n",
    "# initialize our datamodel\n",
    "cifar_model = densenet_cifar()\n",
    "cifar_model = cifar_model.to(device)\n",
    "\n",
    "\n",
    "# use cross entropy as our objective function, since we are building a classifier\n",
    "cifar_criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# use adam as an optimizer, because it is a popular default nowadays\n",
    "# (following the crowd, I know)\n",
    "cifar_optimizer = optim.Adam(cifar_model.parameters(), lr=0.1)\n",
    "best_acc = 0  # save the best test accuracy\n",
    "start_epoch = 0  # start from epoch 0\n",
    "cifar_num_epochs =20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oBGCIXIxhfuz"
   },
   "source": [
    "##Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_ciQnoAjhkxf"
   },
   "outputs": [],
   "source": [
    "#Training for MNIST dataset\n",
    "def train_mnist_model(model, data_loaders, dataset_sizes, criterion, optimizer, num_epochs, device):\n",
    "    \n",
    "    model = model.to(device)\n",
    "    model.train() # set train mode\n",
    "    \n",
    "        \n",
    "  \n",
    "    # for each epoch\n",
    "    for epoch in range(num_epochs):\n",
    "        print('Epoch {}/{}'.format(epoch+1, num_epochs))\n",
    "        \n",
    "        running_loss, running_corrects = 0.0, 0\n",
    "        \n",
    "        # for each batch\n",
    "        for inputs, labels in data_loaders['train']:\n",
    "            inputs = inputs.to(device)\n",
    "            labels =labels.to(device)  \n",
    "\n",
    "            \n",
    "    \n",
    "            # making sure all the gradients of parameter tensors are zero\n",
    "            optimizer.zero_grad() # set gradient as 0\n",
    "            \n",
    "            # get the model output\n",
    "            outputs = model(inputs)\n",
    "            \n",
    "            # get the prediction of model\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            \n",
    "            # calculate loss of the output\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # backpropagation\n",
    "            loss.backward()\n",
    "            \n",
    "            # update model parameters using optimzier\n",
    "            optimizer.step()\n",
    "            \n",
    "            \n",
    "            batch_loss_total = loss.item() * inputs.size(0) # total loss of the batch\n",
    "            running_loss += batch_loss_total # cumluative sum of loss\n",
    "            running_corrects += torch.sum(preds == labels.data) # cumulative sum of correct count\n",
    "        \n",
    "        #calculating the loss and accuracy for the epoch   \n",
    "        epoch_loss = running_loss / dataset_sizes['train']\n",
    "        epoch_acc = running_corrects.double() / dataset_sizes['train']\n",
    "        print('Train Loss: {:.4f} Acc: {:.4f}'.format(epoch_loss, epoch_acc))\n",
    "        print('-' * 10)\n",
    "        \n",
    "    \n",
    "    # after tranining epochs, test epoch starts\n",
    "    else:\n",
    "        model.eval() # set test mode\n",
    "        running_loss, running_corrects = 0.0, 0\n",
    "        \n",
    "        # for each batch\n",
    "        for inputs, labels in data_loaders['test']:\n",
    "            inputs = inputs.to(device)\n",
    "            labels =labels.to(device)\n",
    "\n",
    "    \n",
    "            # same with the training part.\n",
    "            outputs = model(inputs)\n",
    "            _, preds = torch.max(outputs, 1)\n",
    "            loss = criterion(outputs, labels)\n",
    "                    \n",
    "            \n",
    "            running_loss += loss.item() * inputs.size(0) # cumluative sum of loss\n",
    "            running_corrects += torch.sum(preds == labels.data) # cumluative sum of corrects count\n",
    "         \n",
    "        #calculating the loss and accuracy\n",
    "        test_loss = running_loss / dataset_sizes['test']\n",
    "        test_acc = (running_corrects.double() / dataset_sizes['test']).item()\n",
    "        print('<Test Loss: {:.4f} Acc: {:.4f}>'.format(test_loss, test_acc))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1037
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 110371,
     "status": "ok",
     "timestamp": 1560944808379,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "l36skEQEiCGu",
    "outputId": "f7f0432f-1afb-4164-ef7e-0a4ac5165e4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "Train Loss: 0.1181 Acc: 0.9653\n",
      "----------\n",
      "Epoch 2/20\n",
      "Train Loss: 0.1189 Acc: 0.9658\n",
      "----------\n",
      "Epoch 3/20\n",
      "Train Loss: 0.1157 Acc: 0.9658\n",
      "----------\n",
      "Epoch 4/20\n",
      "Train Loss: 0.1159 Acc: 0.9655\n",
      "----------\n",
      "Epoch 5/20\n",
      "Train Loss: 0.1130 Acc: 0.9665\n",
      "----------\n",
      "Epoch 6/20\n",
      "Train Loss: 0.1108 Acc: 0.9676\n",
      "----------\n",
      "Epoch 7/20\n",
      "Train Loss: 0.1104 Acc: 0.9680\n",
      "----------\n",
      "Epoch 8/20\n",
      "Train Loss: 0.1100 Acc: 0.9674\n",
      "----------\n",
      "Epoch 9/20\n",
      "Train Loss: 0.1039 Acc: 0.9688\n",
      "----------\n",
      "Epoch 10/20\n",
      "Train Loss: 0.1064 Acc: 0.9697\n",
      "----------\n",
      "Epoch 11/20\n",
      "Train Loss: 0.1045 Acc: 0.9688\n",
      "----------\n",
      "Epoch 12/20\n",
      "Train Loss: 0.1041 Acc: 0.9693\n",
      "----------\n",
      "Epoch 13/20\n",
      "Train Loss: 0.1047 Acc: 0.9693\n",
      "----------\n",
      "Epoch 14/20\n",
      "Train Loss: 0.1023 Acc: 0.9695\n",
      "----------\n",
      "Epoch 15/20\n",
      "Train Loss: 0.1003 Acc: 0.9708\n",
      "----------\n",
      "Epoch 16/20\n",
      "Train Loss: 0.1008 Acc: 0.9703\n",
      "----------\n",
      "Epoch 17/20\n",
      "Train Loss: 0.0976 Acc: 0.9711\n",
      "----------\n",
      "Epoch 18/20\n",
      "Train Loss: 0.0960 Acc: 0.9714\n",
      "----------\n",
      "Epoch 19/20\n",
      "Train Loss: 0.0946 Acc: 0.9718\n",
      "----------\n",
      "Epoch 20/20\n",
      "Train Loss: 0.0979 Acc: 0.9706\n",
      "----------\n",
      "<Test Loss: 0.0314 Acc: 0.9900>\n"
     ]
    }
   ],
   "source": [
    "train_mnist_model(mnist_model, mnist_dataloaders, mnist_dataset_sizes, mnist_criterion, mnist_optimizer, mnist_num_epochs, device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AxvOthaoHLZD"
   },
   "outputs": [],
   "source": [
    "# Training for CIFAR10 dataset\n",
    "def train_cifar_model(model, train_loader, criterion, optimizer, num_epochs, device):\n",
    "    \n",
    "    print('\\nEpoch: %d' % num_epochs)\n",
    "    model.train() #set the mode to train\n",
    "    train_loss = 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    \n",
    "    for batch_idx, (inputs, targets) in enumerate(train_loader):\n",
    "        inputs, targets = inputs.to(device), targets.to(device)\n",
    "        optimizer.zero_grad() # making sure all the gradients of parameter tensors are zero\n",
    "        outputs = model(inputs) #forward pass the model againt the input\n",
    "        loss = criterion(outputs, targets) #calculate the loss\n",
    "        loss.backward() #back propagation\n",
    "        optimizer.step() #update model parameters using the optimiser\n",
    "\n",
    "        train_loss += loss.item() #cumulative sum of loss\n",
    "        _, predicted = outputs.max(1) #the model prediction\n",
    "        total += targets.size(0)\n",
    "        correct += predicted.eq(targets).sum().item() #cumulative sume of corrects count\n",
    "        if batch_idx % 100 == 0:\n",
    "          #calculating and printig the loss and accuracy\n",
    "          print('Loss: %.3f | Acc: %.3f%% (%d/%d)' % (train_loss/(batch_idx+1), 100.*correct/total, correct, total))\n",
    "\n",
    "#testing for CIFAR10 dataset\n",
    "def test_cifar_model(model, test_loader, criterion, device, save=True):\n",
    "    \"\"\"Tests the model.\n",
    "    Taks the epoch number as a parameter.\n",
    "    \"\"\"\n",
    "    global best_acc\n",
    "    model.eval() # set the mode to test\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        #similar to the train part\n",
    "        for batch_idx, (inputs, targets) in enumerate(test_loader):\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, targets)\n",
    "\n",
    "            test_loss += loss.item()\n",
    "            _, predicted = outputs.max(1)\n",
    "            total += targets.size(0)\n",
    "            correct += predicted.eq(targets).sum().item()\n",
    "\n",
    "            if batch_idx % 100 == 0:\n",
    "                print('Loss: %.3f | Acc: %.3f%% (%d/%d) TEST' % (test_loss/(batch_idx+1), 100.*correct/total, correct, total))\n",
    "    #calculating the accuracy\n",
    "    acc = 100.*correct/total\n",
    "    if acc > best_acc and save:\n",
    "        best_acc = acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2358
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1516986,
     "status": "ok",
     "timestamp": 1560950744837,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "sz4F61bZJ4kn",
    "outputId": "bc004b02-fbb8-4d72-e8f1-37de0b8ec5af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch: 0\n",
      "Loss: 0.251 | Acc: 90.625% (116/128)\n",
      "Loss: 0.228 | Acc: 91.747% (11861/12928)\n",
      "Loss: 0.237 | Acc: 91.581% (23562/25728)\n",
      "Loss: 0.239 | Acc: 91.539% (35268/38528)\n",
      "Loss: 0.665 | Acc: 78.000% (78/100) TEST\n",
      "\n",
      "Epoch: 1\n",
      "Loss: 0.165 | Acc: 96.875% (124/128)\n",
      "Loss: 0.207 | Acc: 92.845% (12003/12928)\n",
      "Loss: 0.221 | Acc: 92.269% (23739/25728)\n",
      "Loss: 0.230 | Acc: 91.873% (35397/38528)\n",
      "Loss: 0.467 | Acc: 87.000% (87/100) TEST\n",
      "\n",
      "Epoch: 2\n",
      "Loss: 0.224 | Acc: 90.625% (116/128)\n",
      "Loss: 0.199 | Acc: 92.907% (12011/12928)\n",
      "Loss: 0.218 | Acc: 92.370% (23765/25728)\n",
      "Loss: 0.219 | Acc: 92.281% (35554/38528)\n",
      "Loss: 0.566 | Acc: 84.000% (84/100) TEST\n",
      "\n",
      "Epoch: 3\n",
      "Loss: 0.227 | Acc: 91.406% (117/128)\n",
      "Loss: 0.193 | Acc: 93.309% (12063/12928)\n",
      "Loss: 0.198 | Acc: 93.148% (23965/25728)\n",
      "Loss: 0.209 | Acc: 92.740% (35731/38528)\n",
      "Loss: 0.487 | Acc: 84.000% (84/100) TEST\n",
      "\n",
      "Epoch: 4\n",
      "Loss: 0.114 | Acc: 97.656% (125/128)\n",
      "Loss: 0.185 | Acc: 93.502% (12088/12928)\n",
      "Loss: 0.195 | Acc: 93.089% (23950/25728)\n",
      "Loss: 0.211 | Acc: 92.515% (35644/38528)\n",
      "Loss: 0.665 | Acc: 81.000% (81/100) TEST\n",
      "\n",
      "Epoch: 5\n",
      "Loss: 0.165 | Acc: 96.875% (124/128)\n",
      "Loss: 0.167 | Acc: 94.137% (12170/12928)\n",
      "Loss: 0.177 | Acc: 93.816% (24137/25728)\n",
      "Loss: 0.185 | Acc: 93.532% (36036/38528)\n",
      "Loss: 0.674 | Acc: 83.000% (83/100) TEST\n",
      "\n",
      "Epoch: 6\n",
      "Loss: 0.123 | Acc: 96.094% (123/128)\n",
      "Loss: 0.169 | Acc: 94.075% (12162/12928)\n",
      "Loss: 0.183 | Acc: 93.493% (24054/25728)\n",
      "Loss: 0.184 | Acc: 93.449% (36004/38528)\n",
      "Loss: 0.598 | Acc: 81.000% (81/100) TEST\n",
      "\n",
      "Epoch: 7\n",
      "Loss: 0.107 | Acc: 96.875% (124/128)\n",
      "Loss: 0.153 | Acc: 94.462% (12212/12928)\n",
      "Loss: 0.161 | Acc: 94.150% (24223/25728)\n",
      "Loss: 0.172 | Acc: 93.901% (36178/38528)\n",
      "Loss: 0.680 | Acc: 82.000% (82/100) TEST\n",
      "\n",
      "Epoch: 8\n",
      "Loss: 0.231 | Acc: 92.188% (118/128)\n",
      "Loss: 0.142 | Acc: 94.848% (12262/12928)\n",
      "Loss: 0.151 | Acc: 94.683% (24360/25728)\n",
      "Loss: 0.160 | Acc: 94.321% (36340/38528)\n",
      "Loss: 0.585 | Acc: 85.000% (85/100) TEST\n",
      "\n",
      "Epoch: 9\n",
      "Loss: 0.146 | Acc: 95.312% (122/128)\n",
      "Loss: 0.151 | Acc: 94.756% (12250/12928)\n",
      "Loss: 0.163 | Acc: 94.314% (24265/25728)\n",
      "Loss: 0.161 | Acc: 94.383% (36364/38528)\n",
      "Loss: 0.536 | Acc: 85.000% (85/100) TEST\n",
      "\n",
      "Epoch: 10\n",
      "Loss: 0.130 | Acc: 95.312% (122/128)\n",
      "Loss: 0.120 | Acc: 95.692% (12371/12928)\n",
      "Loss: 0.134 | Acc: 95.138% (24477/25728)\n",
      "Loss: 0.143 | Acc: 94.874% (36553/38528)\n",
      "Loss: 0.413 | Acc: 90.000% (90/100) TEST\n",
      "\n",
      "Epoch: 11\n",
      "Loss: 0.124 | Acc: 95.312% (122/128)\n",
      "Loss: 0.134 | Acc: 95.336% (12325/12928)\n",
      "Loss: 0.131 | Acc: 95.464% (24561/25728)\n",
      "Loss: 0.140 | Acc: 95.092% (36637/38528)\n",
      "Loss: 0.587 | Acc: 84.000% (84/100) TEST\n",
      "\n",
      "Epoch: 12\n",
      "Loss: 0.099 | Acc: 97.656% (125/128)\n",
      "Loss: 0.125 | Acc: 95.537% (12351/12928)\n",
      "Loss: 0.131 | Acc: 95.359% (24534/25728)\n",
      "Loss: 0.140 | Acc: 95.094% (36638/38528)\n",
      "Loss: 0.440 | Acc: 90.000% (90/100) TEST\n",
      "\n",
      "Epoch: 13\n",
      "Loss: 0.105 | Acc: 97.656% (125/128)\n",
      "Loss: 0.128 | Acc: 95.444% (12339/12928)\n",
      "Loss: 0.129 | Acc: 95.503% (24571/25728)\n",
      "Loss: 0.136 | Acc: 95.307% (36720/38528)\n",
      "Loss: 0.521 | Acc: 85.000% (85/100) TEST\n",
      "\n",
      "Epoch: 14\n",
      "Loss: 0.058 | Acc: 97.656% (125/128)\n",
      "Loss: 0.114 | Acc: 96.047% (12417/12928)\n",
      "Loss: 0.118 | Acc: 95.861% (24663/25728)\n",
      "Loss: 0.124 | Acc: 95.619% (36840/38528)\n",
      "Loss: 0.766 | Acc: 84.000% (84/100) TEST\n",
      "\n",
      "Epoch: 15\n",
      "Loss: 0.149 | Acc: 95.312% (122/128)\n",
      "Loss: 0.118 | Acc: 95.862% (12393/12928)\n",
      "Loss: 0.125 | Acc: 95.631% (24604/25728)\n",
      "Loss: 0.128 | Acc: 95.463% (36780/38528)\n",
      "Loss: 0.833 | Acc: 80.000% (80/100) TEST\n",
      "\n",
      "Epoch: 16\n",
      "Loss: 0.066 | Acc: 98.438% (126/128)\n",
      "Loss: 0.103 | Acc: 96.156% (12431/12928)\n",
      "Loss: 0.104 | Acc: 96.210% (24753/25728)\n",
      "Loss: 0.118 | Acc: 95.751% (36891/38528)\n",
      "Loss: 0.511 | Acc: 90.000% (90/100) TEST\n",
      "\n",
      "Epoch: 17\n",
      "Loss: 0.025 | Acc: 99.219% (127/128)\n",
      "Loss: 0.098 | Acc: 96.597% (12488/12928)\n",
      "Loss: 0.115 | Acc: 96.043% (24710/25728)\n",
      "Loss: 0.125 | Acc: 95.743% (36888/38528)\n",
      "Loss: 0.674 | Acc: 85.000% (85/100) TEST\n",
      "\n",
      "Epoch: 18\n",
      "Loss: 0.074 | Acc: 96.875% (124/128)\n",
      "Loss: 0.097 | Acc: 96.566% (12484/12928)\n",
      "Loss: 0.105 | Acc: 96.311% (24779/25728)\n",
      "Loss: 0.115 | Acc: 96.026% (36997/38528)\n",
      "Loss: 0.691 | Acc: 85.000% (85/100) TEST\n",
      "\n",
      "Epoch: 19\n",
      "Loss: 0.128 | Acc: 96.094% (123/128)\n",
      "Loss: 0.092 | Acc: 96.651% (12495/12928)\n",
      "Loss: 0.104 | Acc: 96.319% (24781/25728)\n",
      "Loss: 0.111 | Acc: 96.107% (37028/38528)\n",
      "Loss: 0.744 | Acc: 85.000% (85/100) TEST\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for epoch in range(start_epoch, start_epoch+cifar_num_epochs):\n",
    "    train_cifar_model(cifar_model, cifar_train_loader, cifar_criterion, cifar_optimizer, epoch, device)\n",
    "    test_cifar_model(cifar_model, cifar_test_loader, cifar_criterion, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "y2CdxjRKinGh"
   },
   "source": [
    "## Save and Reload the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GulJu4twmRsz"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'google.colab'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-4dbea664f495>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Mounting Google Drive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauthenticate_user\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
     ]
    }
   ],
   "source": [
    "# Mounting Google Drive\n",
    "from google.colab import auth\n",
    "auth.authenticate_user()\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')\n",
    "\n",
    "gdrive_dir = 'gdrive/My Drive/ml/'  # update with your own path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sXTIE7tMisI-"
   },
   "outputs": [],
   "source": [
    "# Save and reload the  mnist_model\n",
    "print('==> Saving model for MNIST..')\n",
    "torch.save(mnist_model.state_dict(), gdrive_dir+'lenet_mnist_model.pth')\n",
    "\n",
    "#change the directory to load your own pretrained model\n",
    "print('==> Loading saved model for MNIST..')\n",
    "mnist_model = LeNet().to(device)\n",
    "mnist_model.load_state_dict(torch.load(gdrive_dir+'lenet_mnist_model.pth'))\n",
    "mnist_model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fgKeqp2ALzyL"
   },
   "outputs": [],
   "source": [
    "# Save and reload the  cifar_model\n",
    "print('==> Saving model for CIFAR..')\n",
    "torch.save(cifar_model.state_dict(), './densenet_cifar_model.pth')\n",
    "\n",
    "#change the directory to load your own pretrained model\n",
    "print('==> Loading saved model for CIFAR..')\n",
    "cifar_model = densenet_cifar().to(device)\n",
    "cifar_model.load_state_dict(torch.load(gdrive_dir+'densenet_cifar_model.pth'))\n",
    "cifar_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "udoRJbOPi3KW"
   },
   "source": [
    "## Attack Definition\n",
    "\n",
    "We used these two attack methods:\n",
    "\n",
    "* Fast Gradient Signed Method (FGSM)\n",
    "* Iterative Least Likely method (Iter.L.L.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mouGBnEti2tx"
   },
   "outputs": [],
   "source": [
    "# Fast Gradient Singed Method attack (FGSM)\n",
    "#Model is the trained model for the target dataset\n",
    "#target is the ground truth label of the image\n",
    "#epsilon is the hyper parameter which shows the degree of perturbation\n",
    "\n",
    "def fgsm_attack(model, image, target, epsilon):\n",
    "    # Set requires_grad attribute of tensor. Important for Attack\n",
    "    image.requires_grad = True\n",
    "\n",
    "    # Forward pass the data through the model\n",
    "    output = model(image)\n",
    "    init_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability(the prediction of the model)\n",
    "    \n",
    "    \n",
    "   \n",
    "    # If the initial prediction is already wrong, dont bother attacking\n",
    "    if init_pred[0].item() != target[0].item():\n",
    "    #if init_pred.item() != target.item():\n",
    "        return image\n",
    "    # Calculate the loss\n",
    "    loss = F.nll_loss(output, target)\n",
    "    # Zero all existing gradients\n",
    "    model.zero_grad()\n",
    "    # Calculate gradients of model in backward pass\n",
    "    loss.backward()\n",
    "\n",
    "    # Collect datagrad\n",
    "    data_grad = image.grad.data\n",
    "    \n",
    "    # Collect the element-wise sign of the data gradient\n",
    "    sign_data_grad = data_grad.sign()\n",
    "    # Create the perturbed image by adjusting each pixel of the input image\n",
    "    perturbed_image = image + epsilon*sign_data_grad\n",
    "    # Adding clipping to maintain [0,1] range\n",
    "    perturbed_image = torch.clamp(perturbed_image, 0, 1)\n",
    "    \n",
    "    # Return the perturbed image\n",
    "    return perturbed_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TEdiQORQjK-d"
   },
   "outputs": [],
   "source": [
    "# Iterative least likely method\n",
    "\n",
    "# Model is the trained model for the target dataset\n",
    "# target is the ground truth label of the image\n",
    "# alpha is the hyper parameter which shows the degree of perturbation in each iteration, the value is borrowed from the refrenced paper [4] according to the report file\n",
    "# iters is the no. of iterations\n",
    "# no. of iterations can be set manually, otherwise (if iters==0) this function will take care of it\n",
    "\n",
    "def ill_attack(model, image, target, epsilon, alpha, iters): \n",
    "\n",
    "    # Forward passing the image through model one time to get the least likely labels\n",
    "    output = model(image)\n",
    "    ll_label = torch.min(output, 1)[1] # get the index of the min log-probability    \n",
    "    \n",
    "    if iters == 0 :\n",
    "        # In paper [4], min(epsilon + 4, 1.25*epsilon) is used as number of iterations\n",
    "        iters = int(min(epsilon + 4, 1.25*epsilon))\n",
    "    \n",
    "    # In the original paper the images were in [0,255] range but here our data is in [0,1].\n",
    "    # So we need to scale the epsilon value in a way that suits our data, which is dividing by 255.\n",
    "    epsilon = epsilon/255\n",
    "    \n",
    "    for i in range(iters) : \n",
    "        # Set requires_grad attribute of tensor. Important for Attack\n",
    "        image.requires_grad = True\n",
    "        \n",
    "        # Forward pass the data through the model\n",
    "        output = model(image)\n",
    "        init_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability(the model's prediction)\n",
    "        \n",
    "        # If the current prediction is already wrong, dont bother to continue\n",
    "        if init_pred.item() != target.item():\n",
    "            return image\n",
    "\n",
    "        # Calculate the loss\n",
    "        loss = F.nll_loss(output, ll_label) \n",
    "\n",
    "        # Zero all existing gradients\n",
    "        model.zero_grad()\n",
    "\n",
    "        # Calculate gradients of model in backward pass\n",
    "        loss.backward()\n",
    "\n",
    "        # Collect datagrad\n",
    "        data_grad = image.grad.data\n",
    "\n",
    "        # Collect the element-wise sign of the data gradient\n",
    "        sign_data_grad = data_grad.sign()\n",
    "        # Create the perturbed image by adjusting each pixel of the input image\n",
    "        perturbed_image = image - alpha*sign_data_grad\n",
    "        \n",
    "                \n",
    "        # Updating the image for next iteration\n",
    "        #\n",
    "        # We want to keep the perturbed image in range [image-epsilon, image+epsilon] \n",
    "        # based on the definition of the attack. However the value of image-epsilon \n",
    "        # itself must not fall behind 0, as the data range is [0,1].\n",
    "        # And the value of image+epsilon also must not exceed 1, for the same reason.\n",
    "        # So we clip the perturbed image between the (image-epsilon) clipped to 0 and \n",
    "        # (image+epsilon) clipped to 1.\n",
    "        a = torch.clamp(image - epsilon, min=0)  \n",
    "        b = (perturbed_image>=a).float()*perturbed_image + (a>perturbed_image).float()*a\n",
    "        c = (b > image+epsilon).float()*(image+epsilon) + (image+epsilon >= b).float()*b\n",
    "        image = torch.clamp(c, max=1).detach_()\n",
    "    \n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4eKemT1Hjkjp"
   },
   "source": [
    "## Model Attack Design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hWyQUqMGmq11"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "170500096it [08:17, 267040.80it/s]                               \u001b[A"
     ]
    }
   ],
   "source": [
    "# We used the same values as described in the reference paper [4] in the report.\n",
    "\n",
    "fgsm_epsilons = [0, .05, .1, .15, .2, .25, .3] # values for epsilon hyper-parameter for FGSM attack\n",
    "ill_epsilons = [0, 2, 4, 8, 16] # values for epsilon hyper-parameter for Iter.L.L attack\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_45KGZafjn5M"
   },
   "outputs": [],
   "source": [
    "#This is where we test the effect of the attack on the trained model\n",
    "#model is the pretrained model on your dataset\n",
    "#test_loader contains the test dataset\n",
    "#other parameters are set based on the type of the attack\n",
    "\n",
    "def attack_test(model, device, test_loader, epsilon, iters, attack='fgsm', alpha=1 ):\n",
    "\n",
    "    # Accuracy counter. accumulates the number of correctly predicted exampels\n",
    "    correct = 0\n",
    "    adv_examples = []  # a list to save some of the successful adversarial examples for visualizing purpose\n",
    "    orig_examples = []  # this list keeps the original image before manipulation corresponding to the images in adv_examples list for comparing purpose\n",
    "\n",
    "\n",
    "    # Loop over all examples in test set\n",
    "    for data, target in test_loader:\n",
    "\n",
    "        # Send the data and label to the device\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        # Forward pass the data through the model\n",
    "        output = model(data)\n",
    "        init_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability (model prediction of the image)\n",
    "       \n",
    "      \n",
    "        \n",
    "        # Call the Attack\n",
    "        if attack == 'fgsm':\n",
    "            perturbed_data = fgsm_attack(model, data, target, epsilon=epsilon )\n",
    "        else:\n",
    "            perturbed_data = ill_attack(model, data, target, epsilon, alpha, iters)\n",
    "        \n",
    "\n",
    "        # Re-classify the perturbed image\n",
    "        output = model(perturbed_data)\n",
    "\n",
    "        # Check for success\n",
    "        #target refers to the ground truth label\n",
    "        #init_pred refers to the model prediction of the original image\n",
    "        #final_pred refers to the model prediction of the manipulated image\n",
    "        final_pred = output.max(1, keepdim=True)[1] # get the index of the max log-probability (model prediction of the perturbed image)\n",
    "        if final_pred[0].item() == target[0].item():  #perturbation hasn't affected the classification\n",
    "            correct += 1\n",
    "            \n",
    "            # Special case for saving 0 epsilon examples which is equivalent to no adversarial attack\n",
    "            if (epsilon == 0) and (len(adv_examples) < 5):\n",
    "                adv_ex = perturbed_data.squeeze().detach().cpu().numpy()\n",
    "                orig_ex = data.squeeze().detach().cpu().numpy()\n",
    "                adv_examples.append( (init_pred[0].item(), final_pred[0].item(), adv_ex) )\n",
    "                orig_examples.append( (target[0].item(), init_pred[0].item(), orig_ex) )\n",
    "        else:\n",
    "            # Save some adv examples for visualization later\n",
    "            if len(adv_examples) < 5:\n",
    "                adv_ex = perturbed_data.squeeze().detach().cpu().numpy()\n",
    "                orig_ex = data.squeeze().detach().cpu().numpy()\n",
    "                adv_examples.append( (init_pred[0].item(), final_pred[0].item(), adv_ex) )\n",
    "                orig_examples.append( (target[0].item(), init_pred[0].item(), orig_ex) )\n",
    "\n",
    "    # Calculate final accuracy for this epsilon\n",
    "    final_acc = correct/float(len(test_loader))\n",
    "    print(\"Epsilon: {}\\tTest Accuracy = {} / {} = {}\".format(epsilon, correct, len(test_loader), final_acc))\n",
    "\n",
    "    # Return the accuracy and an adversarial examples and their corresponding original images\n",
    "    return final_acc, adv_examples, orig_examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5EYbvwnEj19f"
   },
   "source": [
    "##Running the Attack for MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 184827,
     "status": "ok",
     "timestamp": 1560947028305,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "m72TMBNyj-Nt",
    "outputId": "dde3b262-6f91-4fb1-a740-b12b1eb6314b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon: 0\tTest Accuracy = 9900 / 10000 = 0.99\n",
      "Epsilon: 0.05\tTest Accuracy = 9375 / 10000 = 0.9375\n",
      "Epsilon: 0.1\tTest Accuracy = 8049 / 10000 = 0.8049\n",
      "Epsilon: 0.15\tTest Accuracy = 5992 / 10000 = 0.5992\n",
      "Epsilon: 0.2\tTest Accuracy = 4014 / 10000 = 0.4014\n",
      "Epsilon: 0.25\tTest Accuracy = 2601 / 10000 = 0.2601\n",
      "Epsilon: 0.3\tTest Accuracy = 1785 / 10000 = 0.1785\n"
     ]
    }
   ],
   "source": [
    "#FGSM attack\n",
    "mnist_fgsm_accuracies = []  #list to keep the model accuracy after attack for each epsilon value\n",
    "mnist_fgsm_examples = []  # list to collect adversarial examples returned from the attack_test function for every epsilon values\n",
    "mnist_fgsm_orig_examples = []  #list to collect original images corresponding the collected adversarial examples\n",
    "\n",
    "# Run test for each epsilon\n",
    "for eps in fgsm_epsilons:\n",
    "    acc, ex, orig = attack_test(mnist_model, device, mnist_test_loader, eps, attack='fgsm', alpha=1, iters=0)\n",
    "    mnist_fgsm_accuracies.append(acc)\n",
    "    mnist_fgsm_examples.append(ex)\n",
    "    mnist_fgsm_orig_examples.append(orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 713606,
     "status": "ok",
     "timestamp": 1560948082239,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "TkED4XKTkRiJ",
    "outputId": "1415b027-4357-43c2-9779-201fa7c0af1c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon: 0\tTest Accuracy = 9900 / 10000 = 0.99\n",
      "Epsilon: 2\tTest Accuracy = 9900 / 10000 = 0.99\n",
      "Epsilon: 4\tTest Accuracy = 9511 / 10000 = 0.9511\n",
      "Epsilon: 8\tTest Accuracy = 241 / 10000 = 0.0241\n",
      "Epsilon: 16\tTest Accuracy = 0 / 10000 = 0.0\n"
     ]
    }
   ],
   "source": [
    "#Iterative_LL attack\n",
    "mnist_ill_accuracies = [] #list to keep the model accuracy after attack for each epsilon value\n",
    "mnist_ill_examples = []  # list to collect adversarial examples returned from the attack_test function for every epsilon values\n",
    "mnist_ill_orig_examples = []  #list to collect original images corresponding the collected adversarial examples\n",
    "\n",
    "# Run test for each epsilon\n",
    "for eps in ill_epsilons:\n",
    "    acc, ex, orig = attack_test(mnist_model, device, mnist_test_loader, eps, attack='ill', alpha=1, iters=0)\n",
    "    mnist_ill_accuracies.append(acc)\n",
    "    mnist_ill_examples.append(ex)\n",
    "    mnist_ill_orig_examples.append(orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_C0flgS6kYpq"
   },
   "source": [
    "##Visualizing the results for MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 877,
     "status": "ok",
     "timestamp": 1560948241301,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "Rf_6SxdXkYIL",
    "outputId": "991d586c-ccc8-4c15-8ce4-ac75eaedcc82"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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279zHLa9NZ9WmnVzy9GTWbdud6NBcFRBlzbQ3sMjMFpvZXmA0MKhImeuAEWa2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\n2jXkbzf2JataOoOf+YwP5q1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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy after attack vs epsilon\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(fgsm_epsilons, mnist_fgsm_accuracies, \"*-\")\n",
    "plt.yticks(np.arange(0, 1.1, step=0.1))\n",
    "plt.xticks(np.arange(0, .35, step=0.05))\n",
    "plt.title(\"FSGM Attack vs MNIST Model Accuracy vs Epsilon\")\n",
    "plt.xlabel(\"Epsilon\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1449
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2223,
     "status": "ok",
     "timestamp": 1560948247268,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "0OlzZ88mVYkw",
    "outputId": "5db53e58-020b-4dcb-8d8e-b71c607dc697"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W4qzv5obtMSgOysll39xGmUTECsBBFAfIQ4qIyRTLDLBjSun6plGmAX+j2Ffs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RNnr4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\nBAoAQAQSKAAAEUigAABE+P/rFCxHW/WQqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x1440 with 14 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot several examples vs their adversarial samples at each epsilon for fgms attack\n",
    "cnt = 0\n",
    "plt.figure(figsize=(8,20))\n",
    "for i in range(len(fgsm_epsilons)):\n",
    "    for j in range(2):\n",
    "        cnt += 1\n",
    "        plt.subplot(len(fgsm_epsilons),2,cnt)\n",
    "        plt.xticks([], [])\n",
    "        plt.yticks([], [])\n",
    "        if j==0:\n",
    "            plt.ylabel(\"Eps: {}\".format(fgsm_epsilons[i]), fontsize=14)\n",
    "    \n",
    "            orig,adv,ex = mnist_fgsm_orig_examples[i][0]\n",
    "            plt.title(\"target \"+\"{} -> {}\".format(orig, adv)+ \" predicted\")\n",
    "            plt.imshow(ex, cmap=\"gray\")\n",
    "        else:\n",
    "            orig,adv,ex = mnist_fgsm_examples[i][0]\n",
    "            plt.title(\"predicted \"+\"{} -> {}\".format(orig, adv)+ \" attacked\")\n",
    "            plt.imshow(ex, cmap=\"gray\")\n",
    "            \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1120,
     "status": "ok",
     "timestamp": 1560948269496,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "Xa6onA4PoY_l",
    "outputId": "6a918b24-2e62-4d29-b122-33c3c4ff20f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KXAgcTzpkca6kWRFRPbjjzyLijIGHbQ1v331hp51SeeW004qOxqylDahHnp3F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\nE7mZWZP7f4S7KcNc3QdQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy after attack vs epsilon\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(ill_epsilons, mnist_ill_accuracies, \"*-\", color='R')\n",
    "plt.yticks(np.arange(0, 1.1, step=0.1))\n",
    "plt.xticks(np.arange(0, 17, step=2))\n",
    "plt.title(\"Iterative Least Likely vs MNIST Model / Accuracy vs Epsilon\")\n",
    "plt.xlabel(\"Epsilon\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1449
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1634,
     "status": "ok",
     "timestamp": 1560948275914,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "q5zzLrnbStw6",
    "outputId": "5db52527-0482-4779-b709-63b91ee5fffc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EBgAAZI+EBgAAZI+EBgAAZI+EBgAAZI+EBgAAZI+EBgAAZI+EBgAAZK9rWzegM1h99dWj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\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot several examples vs their adversarial samples at each epsilon for ill attack\n",
    "cnt = 0\n",
    "plt.figure(figsize=(8,20))\n",
    "for i in range(len(ill_epsilons)):\n",
    "    for j in range(2):\n",
    "        cnt += 1\n",
    "        plt.subplot(len(ill_epsilons),2,cnt)\n",
    "        plt.xticks([], [])\n",
    "        plt.yticks([], [])\n",
    "        if j==0:\n",
    "            plt.ylabel(\"Eps: {}\".format(ill_epsilons[i]), fontsize=14)\n",
    "    \n",
    "            orig,adv,ex = mnist_ill_orig_examples[i][0]\n",
    "            plt.title(\"target \"+\"{} -> {}\".format(orig, adv)+ \" predicted\")\n",
    "            plt.imshow(ex, cmap=\"gray\")\n",
    "        else:\n",
    "            orig,adv,ex = mnist_ill_examples[i][0]\n",
    "            plt.title(\"predicted \"+\"{} -> {}\".format(orig, adv)+ \" attacked\")\n",
    "            plt.imshow(ex, cmap=\"gray\")\n",
    "            \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y-7Ri7J8TmAQ"
   },
   "source": [
    "##Running the Attack for CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 134
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 143630,
     "status": "ok",
     "timestamp": 1560948445596,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "bbp2MFXMTyQm",
    "outputId": "5f13e5b6-e837-4e38-8373-2e1d3296b006"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon: 0\tTest Accuracy = 88 / 100 = 0.88\n",
      "Epsilon: 0.05\tTest Accuracy = 10 / 100 = 0.1\n",
      "Epsilon: 0.1\tTest Accuracy = 12 / 100 = 0.12\n",
      "Epsilon: 0.15\tTest Accuracy = 14 / 100 = 0.14\n",
      "Epsilon: 0.2\tTest Accuracy = 17 / 100 = 0.17\n",
      "Epsilon: 0.25\tTest Accuracy = 10 / 100 = 0.1\n",
      "Epsilon: 0.3\tTest Accuracy = 8 / 100 = 0.08\n"
     ]
    }
   ],
   "source": [
    "#FGSM attack\n",
    "cifar_fgsm_accuracies = [] #list to keep the model accuracy after attack for each epsilon value\n",
    "cifar_fgsm_examples = [] # list to collect adversarial examples returned from the attack_test function for every epsilon values\n",
    "cifar_fgsm_orig_examples = [] #list to collect original images corresponding the collected adversarial examples\n",
    "\n",
    "# Run test for each epsilon\n",
    "for eps in fgsm_epsilons:\n",
    "    acc, ex, orig = attack_test(cifar_model, device, cifar_test_loader, eps, attack='fgsm', alpha=1, iters=0)\n",
    "    cifar_fgsm_accuracies.append(acc)\n",
    "    cifar_fgsm_examples.append(ex)\n",
    "    cifar_fgsm_orig_examples.append(orig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 530559,
     "status": "ok",
     "timestamp": 1560949081435,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "UxF_zJARUY9O",
    "outputId": "584a1745-c4e7-452b-f08f-a98b8c015833"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epsilon: 0\tTest Accuracy = 88 / 100 = 0.88\n",
      "Epsilon: 2\tTest Accuracy = 27 / 100 = 0.27\n",
      "Epsilon: 4\tTest Accuracy = 0 / 100 = 0.0\n",
      "Epsilon: 8\tTest Accuracy = 0 / 100 = 0.0\n",
      "Epsilon: 16\tTest Accuracy = 0 / 100 = 0.0\n"
     ]
    }
   ],
   "source": [
    "#Iterative_LL attack\n",
    "cifar_ill_accuracies = [] #list to keep the model accuracy after attack for each epsilon value\n",
    "cifar_ill_examples = [] # list to collect adversarial examples returned from the attack_test function for every epsilon values\n",
    "cifar_ill_orig_examples = [] #list to collect original images corresponding the collected adversarial examples\n",
    "\n",
    "# Run test for each epsilon\n",
    "for eps in ill_epsilons:\n",
    "    acc, ex, orig = attack_test(cifar_model, device, cifar_test_loader, eps, attack='ill', alpha=1, iters=0)\n",
    "    cifar_ill_accuracies.append(acc)\n",
    "    cifar_ill_examples.append(ex)\n",
    "    cifar_ill_orig_examples.append(orig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5jB__I66Utah"
   },
   "source": [
    "##Visualizing the results for CIFAR10  dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1063,
     "status": "ok",
     "timestamp": 1560949144362,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "h4iR2GFFU4nk",
    "outputId": "515ce883-6b75-40fe-8628-91dfcb0e2620"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LLDcc+Czw4N7EUktamxp5bsVGtmx3F7JZPdqrRLqXjgYWRMTC9B6mNwNnlFju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\nt+vwfl16P4TiYudzKzZw1/w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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy after attack vs epsilon\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(fgsm_epsilons, cifar_fgsm_accuracies, \"*-\")\n",
    "plt.yticks(np.arange(0, 1.1, step=0.1))\n",
    "plt.xticks(np.arange(0, .35, step=0.05))\n",
    "plt.title(\"FSGM Attack vs CIFAR Model Accuracy vs Epsilon\")\n",
    "plt.xlabel(\"Epsilon\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1449
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2555,
     "status": "ok",
     "timestamp": 1560949158162,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "Jtc7cF5aU9Al",
    "outputId": "d0b69ee2-86a6-41fe-b150-5971df81edf5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kYHdTRAtKmhoaTd/DsaPKNjg6o2wjcVt0UVut5+vdf6Fd4vBot56/dkHrYQAA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\n",
      "text/plain": [
       "<Figure size 576x1440 with 14 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot several examples vs their adversarial samples at each epsilon for fgms attack\n",
    "cnt = 0\n",
    "# 8 is the separation between images\n",
    "# 20 is the size of the printed image\n",
    "plt.figure(figsize=(8,20))\n",
    "for i in range(len(fgsm_epsilons)):\n",
    "    for j in range(2):\n",
    "        cnt += 1\n",
    "        plt.subplot(len(fgsm_epsilons),2,cnt)\n",
    "        plt.xticks([], [])\n",
    "        plt.yticks([], [])\n",
    "        if j==0:\n",
    "            plt.ylabel(\"Eps: {}\".format(fgsm_epsilons[i]), fontsize=14)\n",
    "    \n",
    "            orig,adv,ex = cifar_fgsm_orig_examples[i][0]\n",
    "            plt.title(\"target \"+\"{} -> {}\".format(classes[orig], classes[adv])+ \" predicted\")\n",
    "            plt.imshow(ex[0].transpose(1,2,0), cmap=\"gray\")\n",
    "        else:\n",
    "            orig,adv,ex = cifar_fgsm_examples[i][0]\n",
    "            plt.title(\"predicted \"+\"{} -> {}\".format(classes[orig], classes[adv])+ \" attacked\")\n",
    "            plt.imshow(ex[0].transpose(1,2,0), cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1098,
     "status": "ok",
     "timestamp": 1560949186862,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "fsfjkR4VVKlk",
    "outputId": "a15c5194-a360-4f43-86ea-1d6ff6cab0d3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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67EjMrEP0m7wj4rsRMQ3YE7gROBPYXtKlkg4bYNkLgUmSJkoaDkwD5ueW/XBE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\nWtD/B/vJsWst7WuCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Accuracy after attack vs epsilon\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(ill_epsilons, cifar_ill_accuracies, \"*-\", color='R')\n",
    "plt.yticks(np.arange(0, 1.1, step=0.1))\n",
    "plt.xticks(np.arange(0, 17, step=2))\n",
    "plt.title(\"Iterative Least Likely vs CIFAR Model / Accuracy vs Epsilon\")\n",
    "plt.xlabel(\"Epsilon\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1449
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1508,
     "status": "ok",
     "timestamp": 1560949194269,
     "user": {
      "displayName": "Hengame Zabihi",
      "photoUrl": "https://lh4.googleusercontent.com/-SPUHrtHWJKw/AAAAAAAAAAI/AAAAAAAAHS0/SE_z5oPt9c8/s64/photo.jpg",
      "userId": "13748027272382448636"
     },
     "user_tz": -540
    },
    "id": "PAByNYNVVM3E",
    "outputId": "7cb6a073-1a30-438a-e837-b79a2f933479"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vX++nz2q+R0armdl8NTCGMIY4jCGMIdLKP3LaJOnDyzz+EUmbV2VNAAAAHqeV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\nMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x1440 with 10 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot several examples vs their adversarial samples at each epsilon for iterative\n",
    "# least likely attack.\n",
    "\n",
    "cnt = 0\n",
    "# 8 is the separation between images\n",
    "# 20 is the size of the printed image\n",
    "plt.figure(figsize=(8,20))\n",
    "for i in range(len(ill_epsilons)):\n",
    "    for j in range(2):\n",
    "        cnt += 1\n",
    "        plt.subplot(len(ill_epsilons),2,cnt)\n",
    "        plt.xticks([], [])\n",
    "        plt.yticks([], [])\n",
    "        if j==0:\n",
    "            plt.ylabel(\"Eps: {}\".format(ill_epsilons[i]), fontsize=14)\n",
    "    \n",
    "            orig,adv,ex = cifar_ill_orig_examples[i][0]\n",
    "            plt.title(\"target \"+\"{} -> {}\".format(classes[orig], classes[adv])+ \" predicted\")\n",
    "            plt.imshow(ex[0].transpose(1,2,0), cmap=\"gray\")\n",
    "        else:\n",
    "            orig,adv,ex = cifar_ill_examples[i][0]\n",
    "            plt.title(\"predicted \"+\"{} -> {}\".format(classes[orig], classes[adv])+ \" attacked\")\n",
    "            plt.imshow(ex[0].transpose(1,2,0), cmap=\"gray\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "AA-WeSupportVectorMachines!.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
